An alternative strategy for perinatal verbal autopsy coding: single versus multiple coders.
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ABSTRACT:
SUBMITTER: Engmann C
PROVIDER: S-EPMC3927400 | biostudies-literature | 2011 Jan
REPOSITORIES: biostudies-literature
Publications
An alternative strategy for perinatal verbal autopsy coding: single versus multiple coders.
Engmann C C Jehan I I Ditekemena J J Garces A A Phiri M M Mazariegos M M Chomba E E Pasha O O Tshefu A A McClure E M EM Thorsten V V Chakraborty H H Goldenberg R L RL Bose C C Carlo W A WA Wright L L LL
Tropical medicine & international health : TM & IH 20110101 1
<h4>Objective</h4>To determine the comparability between cause of death (COD) by a single physician coder and a two-physician panel, using verbal autopsy.<h4>Methods</h4>The study was conducted between May 2007 and June 2008. Within a week of a perinatal death in 38 rural remote communities in Guatemala, the Democratic Republic of Congo, Zambia and Pakistan, VA questionnaires were completed. Two independent physicians, unaware of the others decisions, assigned an underlying COD, in accordance wi ...[more]
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Project description:IntroductionVerbal autopsy (VA) methods have emerged to estimate causes of death in populations lacking robust civil registration and vital statistics (CRVS) systems. Despite World Health Organization endorsement of routine VA use, cost and efficiency concerns persist. Telephonic verbal autopsies (teleVAs) can reduce cost. Physician coding offers a valuable approach, but the expertise required makes it resource-intensive, often involving multiple coders for consensus.ObjectiveTo assess inter-coder agreement for cause of death (CoD) in South African teleVAs using Kappa statistics, evaluating if agreement surpasses a 0.8 cut-off (very high) potentially allowing single coders.MethodsA cross-sectional study employed telephonic VA interviews on non-facility deaths in Cape Town (December 2020-September 2021). Trained fieldworkers administered a standard VA questionnaire. Each case's VA responses were reviewed independently by two physicians, medically certifying the CoD. A panel was used to solve disagreements. Cohen's kappa-statistic (k-statistic) tested agreement levels.ResultsDecedents were aged between 18 and 98 years. In total, 228 teleVAs (16.6% response rate) were conducted. Physician coding agreement was good overall (k-statistic: 0.63). Diabetes mellitus (47%) and other non-communicable disease (42%) had initial agreement between physician coders in less than 50% of cases in comparison to consensus totals. COVID-19 (89%) and acute cardiac disease (83%) showed initial agreement in more than 80% of cases compared to consensus totals. A chi-square test revealed a significant difference in the number of causes listed on death notification forms for cases with and without agreement in Part 1 (χ2 = 14.71, p < 0.01), but not in Part 2 (χ2 = 4.97, p = 0.17).ConclusionCoD agreement might not be high enough to infer that single coders can be used instead of multiple coders. Challenges with co-morbidities and specific CoDs with multiple sequelae highlight the need for further research and refinement of VA methodologies for reliable CoD determination in routine practice.
| S-EPMC11457238 | biostudies-literature
Project description:BackgroundThe South African national cause of death validation (NCODV 2017/18) project collected a national sample of verbal autopsies (VA) with cause of death (COD) assignment by physician-coded VA (PCVA) and computer-coded VA (CCVA).ObjectiveThe performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard).MethodsSeven performance metrics assessed individual and population level agreement of COD assignment by age, sex and place of death subgroups. Positive predictive value (PPV), sensitivity, overall agreement, kappa, and chance corrected concordance (CCC) assessed individual level agreement. Cause-specific mortality fraction (CSMF) accuracy and Spearman's rank correlation assessed population level agreement.ResultsA total of 5386 VA records were analysed. PCVA and CCVAs all identified HIV/AIDS as the leading COD. CCVA PPV and sensitivity, based on confidence intervals, were comparable except for HIV/AIDS, TB, maternal, diabetes mellitus, other cancers, and some injuries. CCVAs performed well for identifying perinatal deaths, road traffic accidents, suicide and homicide but poorly for pneumonia, other infectious diseases and renal failure. Overall agreement between CCVAs and PCVA for the top single cause (48.2-51.6) indicated comparable weak agreement between methods. Overall agreement, for the top three causes showed moderate agreement for InterVA (70.9) and InSilicoVA (73.8). Agreement based on kappa (-0.05-0.49)and CCC (0.06-0.43) was weak to none for all algorithms and groups. CCVAs had moderate to strong agreement for CSMF accuracy, with InterVA-5 highest for neonates (0.90), Tariff 2.0 highest for adults (0.89) and males (0.84), and InSilicoVA highest for females (0.88), elders (0.83) and out-of-facility deaths (0.85). Rank correlation indicated moderate agreement for adults (0.75-0.79).ConclusionsWhilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa.
| S-EPMC10795603 | biostudies-literature
Project description:BackgroundVerbal Autopsy (VA) is widely viewed as the only immediate strategy for registering cause of death in much of Africa and Asia, where routine physician certification of deaths is not widely practiced. VA involves a lay interview with family or friends after a death, to record essential details of the circumstances. These data can then be processed automatically to arrive at standardized cause of death information.MethodsThe Population Health Metrics Research Consortium (PHMRC) undertook a study at six tertiary hospitals in low- and middle-income countries which documented over 12,000 deaths clinically and subsequently undertook VA interviews. This dataset, now in the public domain, was compared with the WHO 2012 VA standard and the InterVA-4 interpretative model.ResultsThe PHMRC data covered 70% of the WHO 2012 VA input indicators, and categorized cause of death according to PHMRC definitions. After eliminating some problematic or incomplete records, 11,984 VAs were compared. Some of the PHMRC cause definitions, such as 'preterm delivery', differed substantially from the International Classification of Diseases, version 10 equivalent. There were some appreciable inconsistencies between the hospital and VA data, including 20% of the hospital maternal deaths being described as non-pregnant in the VA data. A high proportion of VA cases (66%) reported respiratory symptoms, but only 18% of assigned hospital causes were respiratory-related. Despite these issues, the concordance correlation coefficient between hospital and InterVA-4 cause of death categories was 0.61.ConclusionsThe PHMRC dataset is a valuable reference source for VA methods, but has to be interpreted with care. Inherently inconsistent cases should not be included when using these data to build other VA models. Conversely, models built from these data should be independently evaluated. It is important to distinguish between the internal and external validity of VA models. The effects of using tertiary hospital data, rather than the more usual application of VA to all-community deaths, are hard to evaluate. However, it would still be of value for VA method development to have further studies of population-based post-mortem examinations.
| S-EPMC3912496 | biostudies-literature
Project description:BackgroundThe most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya.MethodsBetween March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (κ) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD.ResultsHCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (κ = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (κ) value of 0.32 (95% CI: 0.30, 0.38). Overall, (κ) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD.ConclusionBoth the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.
| S-EPMC3160942 | biostudies-literature
Project description:BackgroundNigeria has one of the highest under-five mortality rates in the world. Identifying the causes of these deaths is crucial to inform changes in policy documents, design and implementation of appropriate interventions to reduce these deaths. This study aimed to provide national and zonal-level estimates of the causes of under-five death in Nigeria in the 2013-2018 periods.MethodsWe conducted retrospective inquiries into the cause of deaths of 948 neonates and 2,127 children aged 1-59 months as identified in the 2018 Nigeria Demographic and Health Survey (NDHS). The verbal autopsy asked about signs and symptoms during the final illness. The Physician Coded Verbal Autopsy (PCVA) and Expert Algorithm Verbal Autopsy (EAVA) methods were employed to assign the immediate and underlying cause of deaths to all cases.ResultFor the analysis, sampling weights were applied to accommodate non-proportional allocation. Boys accounted for 56 percent of neonatal deaths and 51.5 percent of the 1-59-months old deaths. About one-quarter of under-5 mortality was attributed to neonatal deaths, and 50 percent of these neonatal deaths were recorded within 48 h of delivery. Overall, 84 percent of the under-5 deaths were in the northern geopolitical zones. Based on the two methods for case analysis, neonatal infections (sepsis, pneumonia, and meningitis) were responsible for 44 percent of the neonatal deaths, followed by intrapartum injury (PCVA: 21 percent vs. EAVA: 29 percent). The three main causes of death in children aged 1-59 months were malaria (PCVA: 23 percent vs. EAVA: 35 percent), diarrhoea (PCVA: 17 percent vs. EAVA: 23 percent), and pneumonia (PCVA: 10 percent vs. EAVA: 12 percent). In the North West, where the majority of under-5 (1-59 months) deaths were recorded, diarrhoea was the main cause of death (PCVA: 24.3 percent vs. EAVA: 30 percent).ConclusionThe causes of neonatal and children aged 1-59 months deaths vary across the northern and southern regions. By homing on the specific causes of mortality by region, the study provides crucial information that may be useful in planning appropriately tailored interventions to significantly reduce under-five deaths in Nigeria.
| S-EPMC9172014 | biostudies-literature
Project description:BackgroundVerbal autopsy (VA) has emerged as an increasingly popular technique to assign cause of death in parts of the world where the majority of deaths occur without proper medical certification. The purpose of this study was to examine the key characteristics of studies that have attempted to validate VA cause of death against an established cause of death.MethodsA systematic review was conducted by searching the MEDLINE, EMBASE, Cochrane-library, and Scopus electronic databases. Included studies contained 1) a VA component, 2) a validation component, and 3) original analysis or re-analysis. Characteristics of VA studies were extracted. A total of 527 studies were assessed, and 481 studies screened to give 66 studies selected for data extraction.ResultsSixty-six studies were included from multiple countries. Ten studies used an existing database. Sixteen studies used the World Health Organization VA questionnaire and 5 studies used the Population Health Metrics Research Consortium VA questionnaire. Physician certification was used in 36 studies and computer coded methods were used in 14 studies. Thirty-seven studies used high level comparator data with detailed laboratory investigations.ConclusionMost studies found VA to be an effective cause of death assignment method and compared VA cause of death to a high-quality established cause of death. Nonetheless, there were inconsistencies in the methodologies of the validation studies, and many used poor quality comparison cause of death data. Future VA validation studies should adhere to consistent methodological criteria so that policymakers can easily interpret the findings to select the most appropriate VA method.Prospero registrationCRD42020186886.
| S-EPMC9706899 | biostudies-literature
Project description:BackgroundIn low-resource settings, it is challenging to ascertain the burden and causes of under-5 mortality as many deaths occur outside health facilities. We aimed to determine the causes of childhood deaths in rural Gambia using verbal autopsies (VA).MethodologyWe used WHO VA questionnaires to conduct VAs for deaths under-5 years of age in the Basse and Fuladu West Health and Demographic Surveillance Systems (HDSS) in rural Gambia between September 01, 2019, and December 31, 2021. Using a standardized cause of death list, two physicians assigned causes of death and discordant diagnoses were resolved by consensus.ResultsVAs were conducted for 89% (647/727) of deaths. Of these deaths, 49.5% (n = 319) occurred at home, 50.1% (n = 324) in females, and 32.3% (n = 209) in neonates. Acute respiratory infection including pneumonia (ARIP) (33.7%, n = 137) and diarrhoeal diseases (23.3%, n = 95) were the commonest primary causes of death in the post-neonatal period. In the neonatal period, unspecified perinatal causes of death (34.0%, n = 71) and deaths due to birth asphyxia (27.3%, n = 57) were the commonest causes of death. Severe malnutrition (28.6%, n = 185) was the commonest underlying cause of death. In the neonatal period, deaths due to birth asphyxia (p-value<0.001) and severe anaemia (p-value = 0.03) were more likely to occur at hospitals while unspecified perinatal deaths (p-value = 0.01) were more likely to occur at home. In the post-neonatal period, deaths due to ARIP (p-value = 0.04) and diarrhoeal disease (p-value = 0.001) were more likely to occur among children aged 1-11 months and 12-23 months respectively.ConclusionAccording to VA analysis of deaths identified within two HDSS in rural Gambia, half of deaths amongst children under-5 in rural Gambia occur at home. ARIP and diarrhoea, and the underlying cause of severe malnutrition remain the predominant causes of child mortality. Improved health care and health-seeking behaviour may reduce childhood deaths in rural Gambia.
| S-EPMC10325104 | biostudies-literature
Project description:BackgroundA verbal autopsy (VA) is a post-hoc written interview report of the symptoms preceding a person's death in cases where no official cause of death (CoD) was determined by a physician. Current leading automated VA coding methods primarily use structured data from VAs to assign a CoD category. We present a method to automatically determine CoD categories from VA free-text narratives alone.MethodsAfter preprocessing and spelling correction, our method extracts word frequency counts from the narratives and uses them as input to four different machine learning classifiers: naïve Bayes, random forest, support vector machines, and a neural network.ResultsFor individual CoD classification, our best classifier achieves a sensitivity of.770 for adult deaths for 15 CoD categories (as compared to the current best reported sensitivity of.57), and.662 with 48 WHO categories. When predicting the CoD distribution at the population level, our best classifier achieves.962 cause-specific mortality fraction accuracy for 15 categories and.908 for 48 categories, which is on par with leading CoD distribution estimation methods.ConclusionsOur narrative-based machine learning classifier performs as well as classifiers based on structured data at the individual level. Moreover, our method demonstrates that VA narratives provide important information that can be used by a machine learning system for automated CoD classification. Unlike the structured questionnaire-based methods, this method can be applied to any verbal autopsy dataset, regardless of the collection process or country of origin.
| S-EPMC6617656 | biostudies-literature
Project description:Pressure injuries (PIs) substantively impact quality of care during hospital stays, although only when they are severe or acquired as a result of the hospital stay are they reported as quality indicators. Globally, researchers have repeatedly highlighted the need to invest more in quality improvement, risk assessment, prevention, early detection, and care for PI to avoid the higher costs associated with treatment of PI. Coders' perspectives on quality assurance of the clinical coded PI data have never been investigated. This study aimed to explore challenges that hospital coders face in accurately coding and reporting PI data and subsequently, explore reasons why data sources may vary in their reporting of PI data. This article is based upon data collected as part of a multi-phase collaborative project to build capacity for optimizing PI prevention across Monash Partners health services. We have conducted 16 semi-structured phone interviews with clinical coders recruited from four participating health services located in Melbourne, Australia. One of the main findings was that hospital coders often lacked vital information in clinicians' records needed to code PI and report quality indicators accurately and highlighted the need for quality improvement processes for PI clinical documentation. Nursing documentation improvement is a vital component of the complex capacity building programs on PI prevention in acute care services and is relied on by coders. Coders reported the benefit of inter-professional collaborative workshops, where nurses and coders shared their perspectives. Collaborative workshops had the potential to improve coders' knowledge of PI classification and clinicians' understanding of what information should be included when documenting PI in the medical notes. Our findings identified three methods of quality assurance were important to coders to ensure accuracy of PI reporting: (1) training prior to initiation of coding activity and (2) continued education, and (3) audit and feedback communication about how to handle specific complex cases and complex documentation. From a behavioral perspective, most of the coders reported confidence in their own abilities and were open to changes in coding standards. Transitioning from paper-based to electronic records highlighted the need to improve training of both clinicians and coders.
| S-EPMC9198603 | biostudies-literature
Project description:BackgroundRegistration of the fact of death is almost complete in the city of Chennai and not so in the rural Villupuram district in Tamilnadu, India. The cause of death is often inadequately recorded on the death certificate in developing countries like India. A special verbal autopsy (VA) study of 48,000 adult (aged >or= 25 yrs) deaths in the city of Chennai (urban) during 1995-97 and 32,000 in rural Villupuram during 1997-98 was conducted to arrive at the probable underlying cause of death to estimate cause specific mortality.MethodsA ten day training on writing verbal autopsy (VA) report for adult deaths was given to non-medical graduates with at least 15 years of formal education. They interviewed surviving spouse/close associates of the deceased to write a verbal autopsy report in local language (Tamil) on the complaints, symptoms, signs, duration and treatment details of illness prior to death. Each report was reviewed centrally by two physicians independently. Random re-interviewing of 5% of the VA reports was done to check the reliability and reproducibility of the VA report. The validity of VA diagnosis was assessed only for cancer deaths.ResultsVerbal autopsy reduced the proportion of deaths attributed to unspecified and unknown causes from 54% to 23% (p < 0.0001) in urban and from 41% to 26% (p < 0.0001) in rural areas in Tamilnadu for adult deaths (>or= 25). The sensitivity of VA to identify cancer was 95% in the age group 25-69.ConclusionA ten day training programme to write verbal autopsy report with adequate feed back sessions and random sampling of 5% of the verbal autopsy reports for re-interview worked very well in Tamilnadu, to arrive at the probable underlying cause of death reliably for deaths in early adult life or middle age (25-69 years) and less reliably for older ages (70+). Thus VA is practicable for deaths in early adult life or middle age and is of more limited value in old age.
| S-EPMC535898 | biostudies-literature